Structural Engineering and Mechanics

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Multi-sensor data-based anomaly detection and diagnosis of a pumped storage hydropower plant

  • Sojin Shin (Department of Mechanical Engineering, Korean Advanced Institute for Science and Technology) ;
  • Cheolgyu Hyun (Department of Mechanical Engineering, Korean Advanced Institute for Science and Technolog) ;
  • Seongpil Cho (School of Aerospace and Mechanical Engineering, Korea Aerospace University) ;
  • Phill-Seung Lee (Department of Mechanical Engineering, Korean Advanced Institute for Science and Technolog)
  • Received : 2023.11.01
  • Accepted : 2023.12.02
  • Published : 2023.12.25
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Abstract

This paper introduces a system to detect and diagnose anomalies in pumped storage hydropower plants. We collect data from various types of sensors, including those monitoring temperature, vibration, and power. The data are classified according to the operation modes (pump and turbine operation modes) and normalized to remove the influence of the external environment. To detect anomalies and diagnose their types, we adopt a multivariate normal distribution analysis by learning the distribution of the normal data. The feasibility of the proposed system is evaluated using actual monitoring data of a pumped storage hydropower plant. The proposed system can be used to implement condition monitoring systems for other plants through modifications.

Keywords

Acknowledgement

This research was supported by Korea Hydro & Nuclear Power Co. Ltd. (No. 2017-Tech-11).

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